محمدحسین جهانگیر

The objective of this study is to evaluate the performance of the statistical downscaling models SDSM and LARS-WG in simulating minimum and maximum temperatures and precipitation at four stations (Urmia, Maku, Takab, and Mahabad) in West Azerbaijan province, with data from the periods 1987-2010 and 2020-2065.

The study used the statistical downscaling models SDSM and LARS-WG to simulate temperature and precipitation variables at the selected stations. The observed data for the period 1987-2010 and the forecasted data for 2020-2065 were compared in both models. Additionally, to validate the SDSM model, the simulated parameters were compared with NCEP data and real observed data.

The results showed that both models were more accurate in simulating temperature than precipitation. The SDSM model performed better in simulating daily temperature compared to LARS-WG, whereas the precipitation results from the LARS-WG model were slightly more accurate than those from the SDSM model. Additionally, the RMSE values for the SDSM and LARS-WG models for precipitation were 2.84 mm and 3.4 mm, respectively, while for maximum temperature, the RMSE values were 0.02°C and 0.29°C, respectively. 

Based on the results, the SDSM model demonstrated higher accuracy in simulating both precipitation and temperature compared to the LARS-WG model. This model can be considered a reliable tool for predicting future changes in temperature and precipitation.

One of the most important goals of sustainable development is to preserve nature and improve it, and the manifestation of sustainable development in the built environment basin is called sustainable architecture. Green roof is one of the new approaches to architecture and urban planning and arises from the concepts of sustainable development, which can be used to increase the per capita green space, improve the quality of the environment. Today, due to the increasing need for energy and the limitation of fossil fuels as depleting and polluting sources of the environment, the need to use more renewable energy sources is felt. One of the increasing uses of energy is thermal energy. One of the methods of thermal energy storage is the use of phase change materials. This research has been prepared with the aim of thermal optimization of green roof using phase change materials. By selecting biodegradable materials as phase change materials used in the roof, the research was conducted in three models and three different uses, using Energy Plus software. According to the results, the green roof model optimized with phase change materials in all three different uses of the building has increased the temperature in the building in cold seasons and significantly increased the temperature in hot seasons, and achieved thermal comfort. Therefore, according to the results, using green roof separately causes more temperature balance.

The present study is for the feasibility of using the OWC wave energy converter to supply electrical power to a refinery unit near the shores of the Persian Gulf over a 20-year horizon. Considering the potential of other renewable energy sources in the study area, a combined system of wind turbine, photovoltaic panel, wave converter, electrolyzer, battery and fuel cell is proposed. A comparison between using a reformer instead of an electrolyzer has also been studied. The effect of OWC energy converters on the hybrid system has been evaluated as a new approach in generating the required power. The results show that the hybrid system including the OWC converter with NPC and COE costs $ 26.5 and $ 0.205, respectively, is more cost-effective than the hybrid system without the OWC converter to meet the refinery unit power demand. About 30.7% of the required power is supplied by the wave energy converter. The use of wave energy converters is reduced in the months with low wave energy potential and the role of other renewable energy sources becomes more colorful.

Long and short term weather forecasting have been as two of the most important challenges to water and climate researchers. In order to overcome this challenge, several tools, including atmospheric-ocean general circulation model forecasting scenarios, and downscaling models have been developed and used. These tools downscale forecasting scenarios by creating relationship between parameters of synoptic stations and Large-scale data of general circulation models.

In this study large-scale predictor parameters from 1961 to 2003 from the database of National Centers for Environmental Prediction (NCEP), large-scale data for the A1B and A2 forecast scenarios of the HadCM3 model from 2001 to 2100 from the Canadian Centre for Climate Modelling and Analysis (CCCMA), and the meteorological synoptic data of Ardabil stations from the Meteorological Organization were gathered. To this end, three downscaling models such as Statistical Downscaling Model (SDSM), least squares support vector machine (LS-SVM) and multi-layer perceptron (MLP) were determined for downscaling; and correlation coefficient (CC), Mean Squared Error (MSE), Root Mean Square Error (RMSE), Normalized mean square error (NMSE), Nash-Sutcliffe, Mean Absolute Error (MAE), and Taylor diagram were used to evaluate the efficiency of the models.

The results showed that the MLP obtained the best results based on the average of the stations with the values of (CC=0.85), (NMSE=0.63), (NSH=0.73) and (MAE=0.52), and LS-SVM and SDSM are ranked second and third, respectively. Taylor's diagram also identified the SDSM as the weakest and the LS-SVM and MLP as superior models with a slight difference. Based on downscaling results of all forecasting scenarios, an increase in average daily temperature is also predicted by 2100 in all studied stations.

Based on the results of the study, all forecasting scenarios and all methods of downscaling show increasing the daily average temperature by 2100. Hence, it is necessary to be taken this issue account for making environmental and developing policies in this area.

In the present study, in order to investigate climate change in the western basin of Jazmourian Wetland, the fifth report model of the CanESM2 Intergovernmental Panel on Climate Change and new scenarios of release in the near future (2021-2050) were used. Drought severity classes were followed. Finally, Mann-Kendall no[1]n-parametric test was used to elucidate the trend of annual changes in precipitation component. The results of CanESM2 model show that the average rainfall at Delfard and Dehroud stations has decreased by 0.019 and 0.036% under RCP2.6 scenario. On the other hand, in Kahnooj and Miandeh stations of Jiroft, in this scenario, the rainfall in the next period compared to the base period has increased by 0.085 and 0.17%. These changes in other scenarios have similar decreasing and increasing changes. The results of the rainfall trend indicate that in a number of stations, the trend is significant and in some non-trendy trends that have a downward or upward trend. Also, the results of SPI drought index in the region indicate an increase in drought from humid to more acute conditions. In this index, the highest frequency is related to normal class and the lowest frequency is related to very severe drought. As its situation is more acute in the future. Due to the fact that rainfall has decreased in most stations and on the other hand, the drought in the region has been intensified.

Given the fact that there is a large impact of building energy consumption on our environment and modern society, there is an urgent need for efficient building energy management. The importance of this issue has grown in recent years due to rising concerns about environmental preservation and global warming. Researchers have been researching different strategies in order to achieve optimal energy use in buildings as well as to improve user comfort. This article studies building energy management by examining some studies related this topic and applying their results to a 4-storey office building located in Tehran. DesignBuilder software was used to simulate the building, taking into account the various factors influencing its energy consumption such as the occupants' daily schedules, location, and other external influences affecting thermal comfort conditions. Furthermore, solar panels were digitally incorporated into this simulation in order to find out how much solar energy could be used efficiently in order to provide part of the building's energy needs.

Atmospheric conditions such as climate change, ocean temperatures, changes in the jet stream, and changes in the local landscape are all factors that contribute to drought. Drought not only threatens agriculture but also causes a series of ecological, social, and economic damages. Therefore, it is necessary to have valid and appropriate indicators that can evaluate and measure drought efficiently. Drought monitoring by meteorological and hydrological indicators has always been of interest to researchers. Decreasing or increasing precipitation has a direct effect on river flow rate and groundwater level.

Drought monitoring was performed for 10 meteorological stations and 10 hydrometric stations in the western basin area of Urmia with different distribution functions to identify the best distribution function for fitting data based on standardized precipitation index (SPI) as meteorological drought index and standardized streamflow index (SSI) as hydrological drought index. SPI and SSI values were measured with four cumulative functions, i.e., Gamma, Weibull, logarithmic normal, and Gaussian normal functions. Some performance criteria such as MAE were used to evaluate the efficiency of the functions. This research has been conducted on an annual scale for 31 years from 1989 to 2019. Drought continuity zoning was done through the Inverse distance weighting (IDW) method based on drought continuity.

The results of the SSI showed that the cumulative gamma function had the best performance for fitting data for nine stations. Only at the Merakand station, the Weibull cumulative distribution function provide a better fit for the data. Babarood station with 99% correlation and MSE of 0.017 had the best fit among other stations. In order to understand the drought conditions of the region, according to the selected indicators and based on positive values for wet conditions and negative values for drought conditions, the stations have been studied. Finally, a zoning map of drought continuity is presented.

The SSI and SPI indicators are directly related to each other. The SPI is better for predicting the onset of drought and the SSI is better for measuring the severity and persistence of drought. During the last three decades (1998-2000), Urmia basin has witnessed the highest severity of hydrological drought. Based on the zoning map of the northwest region, especially the upper Chihriq station, it has always enjoyed more favorable conditions and has experienced a milder drought than other regions. However, the extent of severe drought changed from the western part in 1998 to the eastern part in 2000. the severity of drought in 1998 was less than in 2000. In 2000, the drought intensity reached the highest value just in the Merakand station.

General atmospheric circulation models show an increase in greenhouse gas concentrations. These models predict global climate change by simulating the earth's climate. General atmospheric circulation models are not accurate enough for hydrological and water resources studies due to the large temporal and spatial scale of the simulated climate variables compared to the case model. Therefore, they must be scaled. There are different methods for exponential downscale of large-scale variables, and general atmospheric circulation models. In this research, the SDSM model is used to downscaling climatic data.

Boroujerd Synoptic Station with an altitude of 1629 meters above sea level is located at latitude 33◦ 55′ East and longitude 48◦ 45′ North. Boroujerd city is located at the foot of the highest wall of the Zagros at an altitude of 1550 to 1571 meters above sea level and the highest point is in the Garin mountain range with an altitude of 3623 meters in the west and its lowest area is in Silakhor plain with an altitude of 1500 meters. In this study, the CanESM2 climate model under three scenarios RCP26, RCP45, and RCP85 in three time periods 2040-2021, 2060-2041, and 2080-2061; and SDSM model version 4.2 were used for downscale (micro-scale) exponential climatic data. For calibration and validation of the SDSM microscale model, R2 and RMSE calibration indices were used. In this study, 30% of the data were used for validation and 70% of the data were used for calibration.

In the SDSM model, the maximum and minimum temperature values are better predicted than the precipitation values, and the simulated data are closer to the observational values. In all scenarios and periods, the precipitation trend is decreasing. The largest decrease in precipitation is related to January in the period 2021-2040 and the RCP8.5scenario, with a decrease of 69.22 percent. The temperature in all scenarios and periods had an increasing trend compared to the base period. The highest increase in the minimum temperature data is related to the RCP4.5 scenario in October for the period 2061-2080 and it was equal to 4.90 ° C, respectively, and in the maximum temperature data related to the RCP4.5 scenario in October for the period, 2061-2080 was predicted to be equal to 7.02 ° C. Calibration of SDSM model for Boroujerd station for each of the minimum and maximum temperature and precipitation showed the mean values of coefficient of determination 0.99, 0.98 and 0.67, respectively.

The highest decrease in rainfall is related to January in the period 2040-2021 and the RCP8.5 scenario, with a decrease of 69.22%. Also, the SDSM exponential downscale (microscale) model for the minimum and maximum temperature parameters predicted an upward trend. Calibration of SDSM model for Boroujerd synoptic station shows the efficiency of SDSM model in microcompalation of parameters. As a result, we will face a decrease in hydrological reserves in future periods.

Supplying electricity to remote villages that do not have access to the electricity grid has always been a major challenge. Hybridization of renewable resources available in that rural area can be considered as a solution to this challenge.  In the present study, due to the proximity of the study area to Oman Sea and access to suitable wave power for supplying electricity, three types of Wave Energy Converters have been hybridized in Homer Software with solar systems, diesel generators, and batteries. Then, by comparing them, the superior Wave Energy Converter is selected. These Wave Energy Converters, including Pelamis, Wave Dragon, and AquaBuoy, were compared on a 4085 kW photovoltaic system, 9,000 kW diesel generator, and 197 100 kWh batteries to provide a certain amount of electrical load. Finally, the system including Wave Dragon with the energy production cost of 0.240 $ / kWh was chosen as a better economic system.

Drought is one of the major problems facing countries in subtropical offerings such as Iran. Fars province, which is one of the southern provinces in the field of tourism and agriculture, has long been facing the problem of drought and water shortage, so the study of drought in this province to better manage the problem of drought and improve the situation of agriculture and agricultural tourism is necessary. The purpose of this study is to investigate the drought trend in this province using drought index and drought identification index. The parameters used in these indicators are potential rainfall and evapotranspiration. In this regard, rainfall and maximum temperature data of 10 stations in the statistical years 1999-2020 were used and the evapotranspiration of each station was calculated using Larry-Johnson method. Necessary indicators and diagrams were drawn by MATLAB software (a2020) and based on the obtained results, the average maximum and minimum drought intensity occurred in 2008 and 2004, respectively, also in 2020 showed a dry and normal situation. Finally, according to the zoning maps obtained from ArcMap software (10.8), an increase in drought intensity was observed in the southern and central parts of the province.

There are currently various methods for extracting energy and generating electricity from wave energy, one of the simplest and most practical methods is the use of water oscillating column (OWC). The water oscillating column of our study on the OWC has a chamber with a front wall that is oriented against the direction of the current. In this research, a two-dimensional numerical study on the geometric optimization of an ocean wave energy converter (WEC) to electrical energy is presented. The main factor is OWC. To do this, the geometric parameter of the OWC chamber length was optimized, while the other parameters (height to length ratio) were kept constant. In this study, a regular wave with dimensions on a laboratory scale was considered. To determine the optimal energy converter parameter based on environmental parameters (water depth, wave period and altitude), first by using PSO meta-innovation algorithm and coding in MATLAB software to create an algorithm to study and present the most optimal parameters And we did it at different depths. The results of this study indicate the very appropriate answers of the proposed PSO algorithm to determine the parameter of the optimal operating chamber of the wave oscillator energy converter in comparison with other algorithms. The performance of the designed converter in the obtained optimal state is equal to 54%, which increase in comparison with the research background is a self-confirmation of the results obtained from the determination of the optimal state by the proposed algorithm.

Energy consumption in buildings has always been a topic of concern for people and all institutions in the world. The need to pay attention to this issue is due to high energy consumption and significant energy losses in them. The purpose of this study is to calculate the energy consumption of a The educational building and obtaining the amount of energy losses and finally providing a solution to improve energy consumption and reduce it. In line with this purpose, energy audits are used. Finally, suggestions for reducing energy consumption are presented according to the points where losses occur. To build the building, Design Builder software is used and for its energy audit calculations, the relationships between the energy consumption guidelines and the energy label of the National Standards Organization are used. Most of the losses occur from doors, windows and thermal bridges, and by defining three scenarios and implementing them in the building, energy consumption is improved and losses are reduced. Finally, it can be seen that the loss rate is reduced by about 40% and the comfort temperature of the building changes to 20 ° C.

Today, energy is one of the most critical elements of society. With the increase in the world population, energy demand is growing intensely. In this regard, traditional fossil fuel thermal power plants have negative environmental impacts. However, renewable energy systems can produce much cleaner energy using renewable resources. Therefore, countries are transitioning from traditional fossil fuel-based power plants to fully renewable energy systems for a more sustainable world. However, to build an efficient plan for the renewable energy transition, an accurate and comprehensive model is required that can calculate the expected impacts of each strategy to follow. 

EnergyPLAN, an input/output energy system modeling tool with various available components, provides the opportunity to accurately model any energy strategy model. In this study, EnergyPLAN is used to model the energy system of Fars and investigate the potential of the province. Fars is the fourth most populous province in Iran, with an approximate population of 4.8 million. Having 3.26 kWh and 7.21 kWh of minimum and maximum daily solar radiation has made Fars a potential province for solar energy. Also, the annual average wind speed of 5.43 m/s provides opportunities for wind turbine investments. In this study, the energy system of the Fars province in the past, present, and future periods are modeled using EnergyPLAN. For the past modeling, the historical data of energy demands and supplies are used; then, for modeling the current energy systems, the relevant data are gathered and modeled in EnergyPLAN. For future energy system modeling, the realistic data for the Fars province are used in the modeling. Eventually, the environmental impact of these scenarios is compared.
FindingsThe modelings are performed considering the electricity demands and any power plants (for example, thermal power plants, renewable energy systems, and hydropower). Modeling the past scenario indicates that 26.94 MTon CO2 would have been emitted in the past four years. The second scenario is built upon the premise that the prevailing renewable energy systems had been constructed four years ago. The result of this scenario demonstrates a 1.11 MTon less CO2 emission compared to the first scenario. The results of modeling the current energy systems of Fars indicate that existing renewable energy systems in the province are 3% of the total electricity demand. Also, further analysis suggests that in the absence of current renewable energy systems in the province, 280,000 Tons more CO2 will be emitted.For the future scenario, the current data are forecasted for the future, using the previous trend in the growth of demands and supplies. Considering that no new power plants will be built until 2024, the results indicate that the renewable energy fraction will rise to 2.7 % by 2024. However, by using the available data on the construction plans of renewable energy systems until 2024, the results indicate that the renewable energy fraction will reach 14.8 %, and 930,000 Ton less CO2 will be emitted compared to the previous case. 

EnergyPLAN as an accurate energy modeling tool has been used in this study to model the past, current, and future energy flow of Fars province. This has happened by using historical and current data and forecasting future data. The results showed that if the existing renewable energy plants had been built four years sooner, 1.11 MTon less CO2 would have been emitted until now. This demonstrates the role of timing in the construction of renewable energy systems. The result of modeling the current and future energy scenarios indicates that if the current renewable energy systems are built until 2024, 930,000 Ton less CO2 will be emitted. Therefore, considering the province’s high solar and wind potential, it is suggested that more renewable energy systems be built to decrease the CO2 emission cumulatively, and the construction of the traditional power plants should be prohibited. Also, other sources of renewables like biomass and geothermal should be considered in the energy planning to provide a more versatile renewable energy supply to the province. However, the fossil fuel power plants have the advantage of low cost in Iran, making the competition challenging for clean energy sources. Removing the subsidies for fossil fuels can make the competition fairer and provide more opportunities for renewable energy systems to grow in the province.

Due to the advances made in remote sensing technology, collecting information on the quality of surface water resources by this technology, while reducing the cost and time of traditional sampling, can cover all surface water areas. To monitor. In this study, the capability of Sentinel-2 and Landsat-8 satellite images to estimate the concentration of water quality parameters including acidity, electrical conductivity, total soluble solids, alkalinity and surface water temperature were investigated. First, the Sentinel-2 and Landsat-8 satellite images were pre-processed and then the appropriate bands were determined to identify a significant relationship between the values of each water quality parameter and the images were determined using linear regression and their accuracy was calculated for the actual values. The results show the superiority of Sentinel-2 images with Pearson coefficient, standard error and RMSe for PH parameter equal to 0.58, 0.28 and 0.29, EC equal to 0.847, 78.7 and 77.22, TDS equal to 0.895, 45.8 and 44.37 and for the alkalinity parameter only through Landsat-8 images with Pearson coefficient, standard error and RMSe equal to 0.473, 22 and 21.8, respectively. Both satellite images can be used for the water surface temperature parameter due to the high Pearson coefficient. These values are 0.871, 2.9 and 2.85 for Landsat-8 and 0.752 for Sentinel-2, respectively. 0, 14.4 and 4.06.

Climate change conditions have been deteriorated in recent years due to increasing emissions of greenhouse gases, whose negative effects on human societies are one of the major concerns in 21st century, leading to introduction of several scenarios for predicting the climate parameters affected by increasing emissions of greenhouse gases. Therefore, this study sought to investigate the effects of climate change on prospective drought in Tehran province using the Standardized Precipitation and Evapotranspiration Index (SPEI). To this end, daily climate parameters (T-min, T-max, T-mean, and precipitation) of eight synoptic stations were predicted in for the study period (1996-2017), using GCM-based emission Scenarios (RCP2.6, RCP4.5, and RCP8.5) extracted from the IPCC's Fifth report until 2112. Then, the drought's SPEI was calculated based on the predicted parameters, followed by the evaluation of the spatiotemporal characteristics of the drought. A general review of the results showed that the most severe drought would occur in Abali station in July 2073, which would be almost unprecedented in its kind. Moreover, Tehran city would experience more drought stress than other parts of the Tehran province in the coming years. It should be noted that according to the analysis of future drought's time series, "Very dry" months in future would have a 4-month displacement to the backward and would be shifted from September to May than what had been recorded in terms of time period.

This study attempted to predict the precipitation and temperature data at the synoptic station level based on climate change scenarios using SDSM exponential microscopy technique. The section 2 of the article introduces the study area and the stations concerned, the climate change scenarios, the SDSM microscopy model, SPEI drought index, and regional zoning model. In Section 3, the regional drought will be calculated and spatially analyzed based on SPEI index using the predicted data. Finally, the last section of the study is devoted to the summary and general conclusions. Based on the monthly average observational charts and forecasts at each station based on each scenario, it can be claimed that the drought phenomenon is moving backwards in the coming years. In other words, most of the stations are predicted to experience their driest year from September to October. However, according to climate change scenarios, May, June, and July are symbols of high drought months in the coming years.

Temporal Analysis As one of the dimensions of drought characteristics, the detailed drought analysis offers very useful information regarding the intensity, duration, and frequency of drought. According to average monthly observation charts and forecasts prepared for each station in each scenario, it can be argued that the drought phenomenon is moving backwards in the coming years, according to which most stations are predicted to experience their driest years, especially in September and October of each year. However, the climate change scenarios revealed that May, June, and July would be the symbols of high-drought months in the years to come. Spatial analysis based on scenario 2.6 At first glance, it could be said that in all months of the year, the Tehran city would suffer water stress and drought crisis. On the other hand, according to the images obtained, the drought would have a moving trend from January to July, shifting from the west to the east of the province. However, the trend would be concentrated in the west of Tehran province from August to January, except for the December. Spatial analysis based on scenario 4.5 per month Scenario 4.5 reported more severe climate change than Scenario 2.6. The remarkable point in the obtained images was the frequent continuation of drought in the center of Tehran province, i.e., Tehran city. Spatial analysis based on Scenario 8.5 per month Scenario 8.5 shows more different changes in the distribution of drought-prone areas in the coming years than previous climate change scenarios. One of the points to consider in this regard is the significant reduction in the frequency of droughts in the west of Tehran province, which is even lower than those of the center and east parts of the province, being almost the opposite of what was found in the 2.6 scenario.

The comparison of data found for the observation years and the what was predicted for the upcoming years based on different scenarios shows that the frequency of droughts in the coming period. Therefore, if looked more closely, it could be found that the most severe and frequent droughts have occurred throughout the 7th decade of the 21st century, for which proper measures should be devised. The study's results also indicate that the probability of drought in the observation months will change more than what is anticipated, suggesting a seasonal retreat both in drought and wet season. Finally, according to the spatial analysis, it could be said that Tehran city will have higher temperature and precipitation stress (drought) than other parts of Tehran province. On the other hand, with the increase in altitude and the decrease in temperature, the severity of drought will decrease, whose effect on high altitude stations in this study was totally evident.

Covering an area of 45490 square kilometers, the East Azerbaijan province is located in the northwest corner of the Iranian plateau at the range of 45 degrees 7 minutes to 48 degrees 20 minutes’ east longitude and 36 degrees 45 minutes to 39 degrees 26 minutes north latitude, being considered the 10th largest Iranian province. In general, the East Azerbaijan province is a mountainous region, about 40% of whose surface is mountainous, 2.28% of its surface is hilly, and 8.31% of its surface comprises of plains (including mountainous plains). Moreover, the province generally enjoys a cold and dry climate. However, the region has different climates due to its diverse topography. Affected by the cold northern and Siberian winds and the Mediterranean and Atlantic seas' humid winds, the province is a cold and mountainous region that is classified as a semi-arid region in terms of climate, whose average annual precipitation rate is 250-300 mm.

Introduction of SDSM Exponential Micro Scale Model  Weibel et al. (2005) used a multivariate regression model called SDSM to examine the effects of climate change on statistical downscaling, in which the station's daily forecast data (predicted), large-scale NCEP variables (predictor), and large-scale variables of general circulation models under various diffusion scenarios serve as inputs for future study periods. The predictor outputs have many variables. This study used the meteorological data collected from synoptic stations, NCEP data, and CANESM2 data under two RCP 2.6 and RCP 4.5 scenarios. The calibration of the model was performed using the NCEP data. Moreover, the temperature and precipitation rates of Tabriz, Mianeh, and Sarab stations were predicted for the three periods of 2020-2050, 2051-2080, and 2080-2100, which were then compared with the base period. It should be noted that the SDSM model performs better than other models because it combines both regression and probabilistic methods to produce meteorological data, and it is, therefore, one of the best models in this regard compared to other models. Application of the SDSM model to the study basin This study used the SDSM 5/3 model for statistical downscaling. It also used the data regarding the temperature and precipitation collected from three synoptic stations (Tabriz, Mianeh, Sarab), each of which contained 31 statistical years. The SDSM model is performed in several stages, including selecting predictor variables; calibration and validation; model performance review; developing climate scenarios for RCP 2/6 and RCP 4/5 and calibration. Assessing the Performance of the SDSM Model There are various statistical indices for evaluating the performance of observational data, prediction, and error values, including the Index of Agreement (d), Nash-Sutcliffe Performance Index (NSE), Second Root Mean Square Error (RMSE), and Mean Error Average (MAE). In Nash-Sutcliffe Index (NSE) whose variations range from infinite to minus one, the closer the data are to 1, the higher their accuracy would be (Nash et.al, 1970). On the other hand, the value of d ranges from one to zero; Accordingly, the closer the d values are to one, the higher the accuracy and agreement of the predicted and observed data would be.  The average error values ​​between the predicted and observed data are shown by RMSE and MAE. Each of the aforementioned variables is calculated through the following equations. represents monthly precipitation data of Cordex (Ghonchepour et. al., 2019).                                                                                                                                                       

The results obtained from the three meteorological stations indicated that compared to the observational period, the precipitation rate would increase in Tabriz and Mianeh stations and decrease in Sarab station in all three periods under both RCP 2.6 and RCP 4.5 scenarios. Moreover, the temperatures would decrease in Tabriz and Sarab stations in all three periods under both the RCP 2.6 and RCP 4.5 scenarios compared to the observational period, while it would increase in Mianeh station during the same period and under the same scenarios.

The results of this study obtained from the SDSM outputs can be used in hydrological and environmental sectors. They can also be used for predicting climate parameters and assessing climate change, considering the fact that climate change affects water resources, human health, food, and agriculture. Therefore, the study's results could also be used in all of these sectors. On the other hand, as any decline in precipitation rate and increase in temperature would lead to quantitative and qualitative changes in water resources, the water uses plans need to be revised so that they can minimize the harmful effects of such variations.

This research presents a method in sizing a combined on-grid battery and PV system with the aim of providing the needs during the peak load of the network and then estimating the cost and determining the amount of fair subsidies from the government. PV and battery systems that are used in many home, office and commercial sectors today are often connected to the national grid and PV and battery systems are considered as backup systems. The important function of such a system, apart from supplying electricity in the event of a nationwide power outage, is to minimize the cost of electricity consumption by reducing the power consumption during the peak. On the other hand, the function of such systems is to reduce the amount of electricity consumption from the network and reduce the pressure on the network. The first step in installing these systems is to obtain the right size for PV and batteries so that the household load is consumed during the peak and thus minimizes the costs. Then, the cost of the optimal size is calculated, and according to the 4-year return on investment period, the amount of subsidy that the government has to pay for the installation of these systems is calculated. In this paper, we try to simulate the consumption load of a residential house to obtain the appropriate capacity for the PV system and batteries in order to meet the mentioned result.

Drought is a natural disaster that occurs over a long period of time, and affects human societies through negative effects on water resources and agriculture, and consequently the economy. Forests are an important part of the ecosystem that covered about a third of the land surface. Occurrence of wild fires has been degraded the quantity and quality of the forest. The phenomenon of fire is one of the drought complications that cause irreparable damage to the ecosystems. The drought duration and its repetition have been reported more than other natural disasters. The definition of drought has continually been a key issuefor drought monitoring and analysis. The drought is a condition of insufficient moisture caused by a deficit in precipitation over some period of time. Difficulties are primarily related to the time period over which deficits accumulate and to the connection of the deficit in precipitation to deficits in usable water resources and the underlying impacts.

There are several indicators for drought identification and interpretation. The calculation of Keetch–Byram Drought Index (KBDI) requires a long series of meteorological data. When the data are available its calculation is straightforward. However, it is strictly valid for the location of the meteorological station and its immediate vicinity. The temporal and spatial characteristics of drought were investigated using Percent of Normal Precipitation Index (PNPI) and KBDI, at 4 stations in Rasht, Bandar Anzali, Astara and Manjil in Guilan province during a 30-year time period (1987-2017).

According to the results, the drought index KBDI, the monthly time series of the available data were categorized as follows; normal drought class, 96.3%, medium drought, 3.6%, and severe drought, 0.5%. On the other hand, by analyzing the PNPI index, the percentages of normal, moderate, severe and very severe drought classes were 68.9% and 8.1% and 7.5% and 15.4%, respectively, which are almost same as the KBDI results. Taking into account the cumulative percentages of the drought classes of the area, can be deduced from the normal dryness of the area. On the other hand, the most severe dry year, according to the KBDI index was observed in 1988, while 2010 year was the most severe drough year based on the PNPI index. The longest dry period was roughly the same in both indicators, which was determined between 2004 and 2007 years.

The results showed that the probability of severe drought based on the analysis of the past time series is widespread in the months of June and July, which is important in the management of water resources and vegetation. Also, the drought intensity in this province is gradually increasing. Considering the decreasing trend of rainfall in the region, we can expect more droughts in the future. It is recommended that the managers and experts pay special attention to the prevention and management of water resources and vegetation in the two months of July and August during their executive activities.

Climate plans are used in macro-planning, especially for natural disasters (Rezaei et al., 2015). Statistical micro-distribution methods are more efficient due to easy and cheap calculations (Diersing, 2009). One of the statistical models is the SDSM model, which is the relationship between large-scale predictors and local-scale predictors based on multiple linear regression methods. According to research, the SDSM model has an acceptable accuracy in clustering micro data. On the other hand, due to the importance of Yasuj station and its location in Karun catchment area and the need for planning to manage water resources in this basin, the present study uses CanESM2 output, which is one of the climate change models of CMIP5. The IPCC, simulates and examines climate and temperature variables at Yasuj station from 2020 to 2067.​​Study Area Yasuj is a city in southwestern Iran, the capital of Kohgiluyeh and Boyer-Ahmad Province, located on the banks of Bashar River, at the hillsides of the Dena Peak. Yasuj city is located in a cold climate region and has a temperate climate that tends to be cold.
Study

The SDSM microcontroller model was developed in 2002 in the UK by Wilby and Dawson. This model is based on daily local climate data (temperature and precipitation) and large-scale NCEP regional data. The canESM2 model is the fourth generation of climate models developed by the Canadian Center for Climate Modeling and Analysis (CCCMA) and networks the earth in the form of cells measuring 128 x 64 (Charron 2016). In this study, NCEP observational data were used to compile monthly models and canESM2 model outputs were used to predict the amount of variables using SDSM software. NCEP atmospheric variables enter the regression equation of the SDSM model. After selecting the predictors, the observational data of the Yasuj Synoptic Station and the data of the National Center for Predicting Environmental Variables of Canada (NCEP) were calibrated. Then, in order to ensure the calibrated model, temperature and precipitation for the period 2035-2020 were simulated and by comparing the observed and simulated data, the efficiency of the model for Yasuj station was investigated.

In this study, based on the observed data and the global model canESM2, the mean minimum and maximum temperature and average precipitation during the three periods of 2035-2020, 2051-2036 and 2067-2052 were compared with the base period of 2005-2007 under three RCP2 scenarios. RCP2.6, RCP4.5 and RCP8.5 were simulated for Yasuj station and the accuracy of the model was evaluated. The maximum agreement is at the minimum and maximum temperatures of the observed and simulated data, which show the appropriate and acceptable efficiency of simulating the desired climatic parameters for the period. In general, the amount of precipitation will increase in all studied future periods. This increase will be more evident than RCP2.6 according to the RCP4.5 and RCP8.5 scenarios. In general, the maximum minimum temperature during the period 2035-2020 shows an increasing anomaly of about 0.5 degrees and in the future periods 2051-2036 and 2067-2052 it shows a decrease compared to the base period. The lowest minimum temperature is estimated for January 2035-2020 under the RCP8.5 scenario.The maximum temperature of Yasuj station during the periods shows an increase. Incremental changes are less in June and August and more in January to May as well as in October, November and December. Of course, these changes are more noticeable in November and April. The highest temperature in the coming years will be related to July of 2051-2036 under the RCP4.5 scenario.

According to the results, it was found that precipitation in the coming years will show an increasing anomaly, which is faster in the first period and slower in the final periods.Slight changes in precipitation along with increasing temperature have affected the quality of water resources. This is due to the importance of this station in Karun catchment. Therefore future planning of water resources management should deal with the least quantitative and qualitative effects of water resources in the basin.

The protection of historical, cultural and natural heritage is a vital necessity in the present era. Carrying this operation out has great enconomic, social and cultural benefits that will play a role as an effective factor to establish sustainable development. In the current years, Iran follows the development prospects programs to expand its non oil industries such as cultural heritage industry that will play an important role in the sustainable development, and decrease its maladaptive and humongous dependence on oil export revenue. Since protection of historical remains and preservation of heritage of the Iranian civilization is a public duty, boosting public participation in the safekeeping process would be highly effective in protection and sustainable prosperity of the aforementioned heritages. As sustainable development is a multidisciplinary paradigm, a multi-attribute decision making approach is needed. The current study, implements a combination of hierarchical structure and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to respectively obtain the significance of sustainable development’s attributes, and the options of maximizing the public's involvement. Based on the results, protection of natural resources and use of the various potentials of the social networks was the most important attribute and option, respectively. Due to relative sustainability of the ranking lists after performing the sensitivity analysis, it could be concluded that the decision making process was done properly. Hence, this approach is recommended for other managerial and conservation issues in different fields of civil engineering and architecture.

It is necessary the (GCMs) act on the local behavior of the station data, to understand the status of a local station using these models. In this study using SDSM, LARS_WG models and artificial neural network method Based on observational data obtained from 6 synoptic stations in Isfahan province, temperature and precipitation forecasts of these stations from 2005 to 2050 have been investigated. Pearson's correlation coefficient (r) and coefficient of determination (R2) between observational data and scenario data at all stations are above 0.6.The results show that the SDSM model is more efficient for predicting temperature than the other two models. Based on the results, the temperature of Isfahan province increased from 2005 to 2050 and In 2005 it had the lowest value and in 2050 it had the highest value and the temperature difference of these years is about 2/3°C, Precipitation changes are almost similar to current changes and we will have the highest amount of precipitation in 2024 and 2028 which, in daran statian which, it has more rainfall than other stations, it will be about 2/5 mm, therefore, Isfahan province needs appropriate management strategies to reduce water consumption to prevent the possibility of dehydration and drought in the years ahead.

Ecotourism is one of the fastest growing trends in the worldwide tourism industry. The environment has an intrinsic value which outweighs its value as a tourism asset. It is also agreed upon by environmental experts that the three elements, viz., political acceptability, social responsibility and sound environment with a perfect balance between quality and quantity, in tourism development, promotion, can ensure quality tourism and also it is one of the fastest growing trends in the worldwide tourism industry. This paper is inscribed with the aim of investigating ecotourism capabilities in Chamran Park as a green and modern recreational region and tried to evaluate this region as an economic, social, cultural, ecological area. For regarding to the purposes, use two types of strategic management method that are utilized to analyze management solutions are called SWOT method and stakeholder matrix. The results of weighted scores was represented that strength points with weighted scores of 3.25, opportunities with weighted scores 2.52, weakness points with weighted scores 1.94 and threats with weighted scores 1.7 were located in the order of first to fourth positions. Thus, it is afforded to process ecotourism management condition of Chamran Park by evaluating ecotourism capabilities of this region and introduce this region as one of the suburban and urban tourism destinations in Karaj city.

The present research aims to study the trend of precipitation changes of Karkheh Catchment Area in 19 next years via using 6 synoptic stations (Eslam Abad Gharb, Bostan, Khorram Abad, Ravansar, Kangavar, and Malayer). At first, climate data of the basic period from 1998 to 2016 was provided daily. Then, output data of IPCM4 Atmosphere General Circulation Model was become microscale with 3 scenarios of climate change of A1B, A2, and B1 through LARS-WG Model in Karkheh Catchment Area. Obtained results of precipitation changes were evaluated and analyzed in 6 synoptic stations of Karkheh Catchment Area in basic period of 1998-2016 and next period of 2017-2035. Studying of error rate of observation and simulated data was conducted in evaluation of LARS-WG Model and the proper model was evaluated. Obtained results of studying precipitation average represents that the precipitation amount of the next 19 years will increase compared to the 19 previous years based on each of three scenarios of A1B, A2 and B1 in stations of Eslam Abad Gharb, Bostan, Ravansar and Malayer and A2 Scenario shows more increase in comparison to the other scenarios. Indeed, precipitation amount of the next period will decrease compared to the previous period in Kangavar Station, according to each of three scenarios of A1B, A2, and B1.

By analyzing the historical records of the drought phenomenon and relying on the available statistics and information, it is possible to estimate the period of drought returns for different regions. By creating drought preparedness plans, the problems caused by drought can be greatly reduced. Frequency along with severity, duration and range are the most important features studied in drought studies. The purpose of this study was to calculate the relative drought frequencies of the Persian Gulf basin with the help of the standardized precipitation evapotranspiration drought index (SPETI)  and to prepare a map of their frequencies. For this purpose, SPETI index was used as a selected index to study droughts in 10 stations located inside and outside the basin with a common statistical period of 30 years (1988-2017) in four scales of 12, 9, 6 and 3 months. After reviewing the output results, the relative frequencies of drought were extracted and mapped at each scale. Trend graphs of precipitation and temperature showed that the annual data of precipitation and temperature had a decreasing and increasing trend, respectively, with a temperature increase of 28%; hence, the results showed that SPETI index in presenting drought phenomena was faced with an increase in temperature and a decrease in precipitation. Therefore, these stations are introduced as stations with the potential for drought sensitivity.